Submission

Put the ipynb file and html file in the github branch you created in the last assignment and submit the link to the commit in brightspace

from plotly.offline import init_notebook_mode
import plotly.io as pio
import plotly.express as px

init_notebook_mode(connected=True)
pio.renderers.default = "plotly_mimetype+notebook"

#load data
df = px.data.gapminder()
df.head()

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
0	Afghanistan	Asia	1952	28.801	8425333	779.445314	AFG	4
1	Afghanistan	Asia	1957	30.332	9240934	820.853030	AFG	4
2	Afghanistan	Asia	1962	31.997	10267083	853.100710	AFG	4
3	Afghanistan	Asia	1967	34.020	11537966	836.197138	AFG	4
4	Afghanistan	Asia	1972	36.088	13079460	739.981106	AFG	4

Question 1:

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

• Extract the 2007 year data from the dataframe. You have to process the data accordingly
• use plotly bar
• Add different colors for different continents
• Sort the order of the continent for the visualisation. Use axis layout setting
• Add text to each bar that represents the population

df_2007 = df.query('year==2007')
df_new = df_2007.groupby("continent").sum()
fig = px.bar(
    df_new, x= "pop", y = df_new.index, orientation = 'h', 
    color= df_new.index
)
fig.show()

Question 2:

Sort the order of the continent for the visualisation

Hint: Use axis layout setting

# YOUR CODE HERE
df_2007 = df.query('year==2007')
df_new = df_2007.groupby("continent").sum()
fig = px.bar(
    df_new, x= "pop", y = df_new.index, orientation = 'h', 
    color= df_new.index
)
fig.update_yaxes(categoryorder="total ascending")
fig.show()

Question 3:

Add text to each bar that represents the population

# YOUR CODE HERE
df_2007 = df.query('year==2007')
df_new = df_2007.groupby("continent").sum()
fig = px.bar(
    df_new, x= "pop", y = df_new.index, orientation = 'h', 
    color= df_new.index, text_auto=".2s" 
)
fig.update_traces(textfont_size=12, textangle=0, textposition="outside", cliponaxis=False)
fig.update_yaxes(categoryorder="total ascending")
fig.show()

Question 4:

Thus far we looked at data from one year (2007). Lets create an animation to see the population growth of the continents through the years

# YOUR CODE HERE
df = px.data.gapminder()
df_new = df.groupby(['continent', 'year'], as_index = False)['pop'].sum()
print(df_new)

fig = px.bar(df_new, x= "pop", y = "continent", animation_frame="year", animation_group= "continent", 
           color="continent", 
           range_x=[0,4000000000])
fig.update_yaxes(categoryorder="total ascending")
fig.show()

   continent  year         pop
0     Africa  1952   237640501
1     Africa  1957   264837738
2     Africa  1962   296516865
3     Africa  1967   335289489
4     Africa  1972   379879541
5     Africa  1977   433061021
6     Africa  1982   499348587
7     Africa  1987   574834110
8     Africa  1992   659081517
9     Africa  1997   743832984
10    Africa  2002   833723916
11    Africa  2007   929539692
12  Americas  1952   345152446
13  Americas  1957   386953916
14  Americas  1962   433270254
15  Americas  1967   480746623
16  Americas  1972   529384210
17  Americas  1977   578067699
18  Americas  1982   630290920
19  Americas  1987   682753971
20  Americas  1992   739274104
21  Americas  1997   796900410
22  Americas  2002   849772762
23  Americas  2007   898871184
24      Asia  1952  1395357351
25      Asia  1957  1562780599
26      Asia  1962  1696357182
27      Asia  1967  1905662900
28      Asia  1972  2150972248
29      Asia  1977  2384513556
30      Asia  1982  2610135582
31      Asia  1987  2871220762
32      Asia  1992  3133292191
33      Asia  1997  3383285500
34      Asia  2002  3601802203
35      Asia  2007  3811953827
36    Europe  1952   418120846
37    Europe  1957   437890351
38    Europe  1962   460355155
39    Europe  1967   481178958
40    Europe  1972   500635059
41    Europe  1977   517164531
42    Europe  1982   531266901
43    Europe  1987   543094160
44    Europe  1992   558142797
45    Europe  1997   568944148
46    Europe  2002   578223869
47    Europe  2007   586098529
48   Oceania  1952    10686006
49   Oceania  1957    11941976
50   Oceania  1962    13283518
51   Oceania  1967    14600414
52   Oceania  1972    16106100
53   Oceania  1977    17239000
54   Oceania  1982    18394850
55   Oceania  1987    19574415
56   Oceania  1992    20919651
57   Oceania  1997    22241430
58   Oceania  2002    23454829
59   Oceania  2007    24549947

Question 5:

Instead of the continents, lets look at individual countries. Create an animation that shows the population growth of the countries through the years

df = px.data.gapminder()
fig = px.bar(
    df, x= "pop", y = "country", orientation = 'h', animation_frame="year", animation_group= "country", hover_name="country", 
    color= "country", range_x=[0, 1400000000]
)
fig.update_yaxes(categoryorder="total ascending")
fig.show()

Question 6:

Clean up the country animation. Set the height size of the figure to 1000 to have a better view of the animation

df = px.data.gapminder()
fig = px.bar(
    df, x= "pop", y = "country", orientation = 'h', animation_frame="year", animation_group= "country", hover_name="country", 
    color= "country", range_x=[0, 1400000000], height=1000
)
fig.update_yaxes(categoryorder="total ascending")

fig.show()

Question 7:

Show only the top 10 countries in the animation

Hint: Use the axis limit to set this.

# YOUR CODE HERE
import plotly.graph_objects as go
df = px.data.gapminder()
n = len(df.country.unique())
fig = px.bar(
    df, x= "pop", y = "country", orientation = 'h', animation_frame="year", animation_group= "country", hover_name="country", 
    color= "country", range_x=[0, 1400000000], range_y=[n-10, n]
)
fig.update_yaxes(categoryorder="total ascending")

fig.show()